**Price:**$2,999.00

**Length:**3 Days

**Machine Learning for Control Training**

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.

This is made possible through the correct application of advanced algorithms, which find and apply patterns in data. These algorithms are essentially the heartbeat of machine learning applications.

Machine learning algorithms are programs that can learn from data and improve from experience without human intervention. Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data; or instance-based learning where a class label is produced for a new instance by comparing the new instance to instances from the training data, which were stored in memory.

There are two general categories of machine learning. Supervised machine learning techniques are applied when there’s a piece of data that we want to explain or predict. This is usually done by using previous data of inputs and outputs to predict an output based on a new input.

Contrasted with supervised machine learning, the second category, unsupervised machine learning, looks at ways to relate and group data points without the use of a target variable to predict. It evaluates data in terms of traits and uses the traits to form clusters of items that are similar to one another.

For the data scientist, there are several machine learning methods that need to be understood. One of the most common is the regression method, which falls within the category of supervised machine learning. This method helps to predict or explain a particular numerical value based on a set of prior data. This is helpful to realtors and brokers wanting to predict the price of a property based on previous pricing data for similar properties.

A linear regression model is trained with many data pairs **(x, y)** by calculating the position and slope of a line that minimizes the total distance between all of the data points and the line. In other words, we calculate the slope (**m**) and the y-intercept (**b**) for a line that best approximates the observations in the data.

Regression techniques run the gamut from simple like linear regression to complex like regularized linear regression, polynomial regression, decision trees and random forest regressions, neural nets, among others.

Machine learning methodologies powered by algorithms will be responsible for self-driving vehicles and much of the advanced architecture used for Smart cities. But you don’t have to wait until the future to experience the manifestations of machine learning algorithms. Some of the applications beings used right now include:

- Virtual personal assistants like Alexa and Siri
- Email spam and malware filtering
- Search engine result refining
- Product recommendations
- Online fraud detection
- Traffic and weather predictions

**Machine Learning for Control Training Course by Tonex **

Machine Learning for Control Training is a 3-day technical training course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI), and the fundamentals of control theory, an area of engineering related to control of continuously operating dynamical systems in engineered processes and machines.

Moreover, Machine Learning for Control Training will focus on the intersection of both fields and will describe current and state-of-the-art techniques for implementing machine learning for control applications. Key applications include complex nonlinear systems for which classical linear control theory methods may not be readily applicable.

Machine learning serves to automate the data analysis process by enabling computers and machines to learn from data through experience applied to specific tasks without explicit programming. Control theory serves to control processes and devices (e.g., motors, robots, flight controls, etc.) using sophisticated mathematical techniques and models.

For systems where the mathematical techniques are too computationally complex or are undetermined, machine learning can serve as an input to algorithms in order to control complex dynamical systems.

Attendees will learn, comprehend and master ideas on machine learning concepts, key principles, and techniques including: supervised and unsupervised learning, mathematical and heuristic aspects of data analysis, modeling to describe key algorithms such as linear regression, clustering, classification, and prediction.

Further, attendees will be learning ideas on control theory concepts, including: linear systems control, system identification, open-loop and closed-loop control, non-linear control, system stability considerations, and main control techniques (e.g., adaptive control, intelligent control, optimal control, robust control, etc.).

Additionally, attendees will learn how to adapt machine learning techniques to control applications (e.g., flying a drone, autonomous vehicles, and the like). Machine learning for control provides techniques for computers to learn about big data sets without being programmed explicitly, for example, by using methods of data analysis.

Further machine learning for control applies the information gathered from the big data to control problems of high complexity. Accordingly, such techniques take advantage of data mining approaches, statistics, and other machine learning algorithms to build models for predicting future outcomes for control.

Linear algebra and computer programming are the basis for many of the machine learning for control algorithms. Using machine learning as a tool, the computer must automatically learn the parameters of models from the data. Using larger datasets, better accuracy and performance can be achieved.

Machine learning for control, for example, can be used in proactive maintenance to continuously monitor the performance of simple or complex industrial systems, applications and events. Using the ability to learn and adapt, makes it the optimal choice for improvements in ongoing processes, and to automatically predict and prevent failures.

**Learning Objectives**

After completing this course, the student will be able to:

- Learn about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
- List similarities and differences between AI, Machine Learning and Data Mining
- Learn how Artificial Intelligence uses data to offer solutions to existing problems
- Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn, adapt and optimize
- Clarify how Data Mining can serve as foundation for AI and machine learning to use existing information to highlight patterns
- Learn the basics of classical control theory
- Learn the basics of mathematical concepts common to both control theory and machine learning including linear algebra concepts and calculus concepts
- Learn how to classify the types of learning such as supervised and unsupervised learning
- Make accurate predictions and analysis to effectively solve potential problems
- List Machine Learning concepts, principles, algorithms, tools and applications
- Learn the concepts and operation of various machine learning techniques and algorithms most adaptable to control theory, including, but not limited to, neural networks of various kinds (convolutional, recurrent, long-short term memory, etc.), support vector machines (including non-linear extensions such as kernel support vector machines), probabilistic methods such as naive Bayes and deep belief networks, reinforcement learning
- Comprehend the theoretical concepts of both machine learning and control theory and how they relate to the practical aspects controlling complex dynamical systems

** COURSE OUTLINE** ** **

**The Basics of Machine Learning**

- What is Machine Learning?
- Emergence and applications of Artificial Intelligence and Machine Learning
- Basics of Artificial Intelligence
- Basics of Machine Learning
- Basics of Data Mining
- Data Mining versus Machine Learning versus Data Science
- Data Mining and patterns
- Why is machine learning important?
- Creating good machine learning systems

**Popular Machine Learning Methods**

- Supervised learning and unsupervised learning
- Supervised learning algorithms and labeled data
- Trained using labeled examples
- Classification, regression, prediction and gradient boosting
- Supervised learning and patterns
- Predicting the values of the label on additional unlabeled data
- Using historical data to predict likely future events
- Unsupervised learning and unlabeled data
- Unsupervised learning against data that has no historical labels
- Semi supervised learning
- Using both labeled and unlabeled data for training
- Classification, regression and prediction
- Reinforcement learning
- Robotics, gaming and navigation
- Discovery through trial and error
- The agent (the learner or decision maker)
- The environment (everything the agent interacts with)
- Actions (what the agent can do)

**Review of Terminology and Principles**

- Math Refresher
- Concepts of linear algebra
- Probability and statistics
- Algorithms
- Automation and iterative processes
- Scalability
- Ensemble modeling
- Framing
- Generalization
- Machine Learning methods
- Classification
- Training and Training Set
- Validation
- Representation
- Regularization
- Logistic Regressions
- Neutral Nets
- Multi class Neutral Nets
- Embeddings
- Basic Algebra and Calculus
- Basic Python
- Chain rule
- Concept of a derivative
- Gradient or slope
- Linear algebra
- Logarithms and logarithmic equations
- Matrix multiplication
- Mean, median, outliers and standard deviation
- Partial derivatives
- Sigmoid function
- Statistics
- Tanh
- Tensor and tensor rank
- Trigonometry
- Variables, coefficients, and functions

**Machine Learning Concepts Related to Control**

- Neural networks
- Feedforward neural network
- Backpropagation
- Cost function
- Weights, bias, activation function
- Gradients
- Vanishing and exploding gradients
- Stochastic gradient
- Convolutional neural network
- Recurrent neural network
- Long short-term memory (LSTM) networks
- Fully recurrent neural networks
- Elman networks and Jordan networks
- Hopfield network
- Bidirectional associative memory (BAM) network
- Gated recurrent unit
- Reinforcement learning

**Introduction to Control**

- State-space representation
- Open-loop control vs closed loop control
- State variables
- Linear systems
- Linear time-invariant theory
- Continuous-time LTI case
- Step response
- Impulse response
- Phase space

**Types of controllers**

- Lead-lag compensator
- Programmable logic controller
- Embedded controller

**Frequency-Domain Approach to Control**

- Transfer function
- Closed loop transfer function
- Z-transform
- Laplace transform

**Stability**

- Stability theory
- Bounded-input bounded-output (BIBO) stability
- Input-to-state stability (ISS)
- Bode plot
- Nichols plot
- Nyquist plot
- Routh–Hurwitz stability criterion
- Root locus analysis: angle condition, magnitude condition
- Gain margin and phase margin
- Nyquist stability criterion
- Root locus method
- Lyapunov stability

**Machine Learning Control Basics**

- Complex non-linear dynamical systems
- Classical Approaches: Chaos theory
- Classical Approaches: Lorenz attractor
- Nonlinear, multivariable, adaptive and robust control theories
- Full state feedback (FSF)
- Pole placement

**Machine learning control**

- Intelligent control
- Neural network control
- Hidden Markov models (HMMs)
- Kalman filter
- Kalman gain
- Non-linear Kalman filter
- Extended Kalman filter
- Unscented Kalman filter
- Particle filter
- Reinforcement learning control

Machine Learning for Control Training